{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "90a8b46d-e730-448f-91bd-455448a404af",
   "metadata": {},
   "source": [
    "# Accelerated Backtesting\n",
    "\n",
    "## Overview\n",
    "There is a trade-off between the accuracy and speed of backtesting. *hftbacktest* provides highly accurate results, but it is relatively slow, making it challenging to quickly test new ideas or optimize parameters through rapid iteration. To improve backtest speed, this approach excludes certain conditions, sacrificing a certain degree of accuracy.\n",
    "\n",
    "The main performance gain comes from precomputing fill conditions for a given running interval. This includes ignoring both fills in the queue position and the order response latency, enabling backtesting within a single loop iteration. By removing queue position estimation, we eliminate the need to fully replay the market depth feed or process every depth update.\n",
    "\n",
    "Thus, this approach accounts for feed latency and order entry latency, but not order-response latency. In the fill simulation, queue position is not modeled, so partial fills do not occur—orders are either fully filled when crossed or not filled at all.\n",
    "\n",
    "While these simplifications can result in a loss of accuracy—particularly in case that queue position fills are critical typically due to large tick sizes—they offer substantial performance gains."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de657928-c603-4c0a-ab62-b13b6c03c84c",
   "metadata": {},
   "source": [
    "## Fill Conditions\n",
    "\n",
    "- A **buy** order is eligible to fill when **order price >= best ask** or **order price > sell trade price**.  \n",
    "- A **sell** order is eligible to fill when **order price <= best bid** or **order price < buy trade price**.\n",
    "\n",
    "Because queue position is not considered, equality does not count:\n",
    "\n",
    "- buy price == sell trade price → **not filled**\n",
    "- sell price == buy trade price → **not filled**\n",
    "\n",
    "In short, if the market price crosses the order price (strictly), the order is considered filled.\n",
    "\n",
    "## Limit Order Fill Prices\n",
    "For a given time interval [t_i, t_{i+1}]:\n",
    "\n",
    "**Bid fill price (applies to buy orders):**\n",
    "```\n",
    "bid fill price = min(\n",
    "    lowest best ask at the exchange over the interval,\n",
    "    (lowest sell trade price + one tick) at the exchange over the interval\n",
    ")\n",
    "```\n",
    "An open buy order with **order price >= bid fill price** is considered filled in the interval.  \n",
    "\n",
    "**Ask fill price (applies to sell orders):**\n",
    "\n",
    "```\n",
    "ask fill price = max(\n",
    "    highest best bid at the exchange over the interval,\n",
    "    (highest buy trade price - one tick) at the exchange over the interval\n",
    ")\n",
    "```\n",
    "An open sell order with **order price <= ask fill price** is considered filled in the interval.\n",
    "\n",
    "## Order Response Latency\n",
    "To maintain a single state (no separate local and exchange state) and utilize a single-loop iteration, this don't account for order response latency. Consequently, all state changes—order acceptance, cancellation, fills, and position updates—are reflected immediately on the local side.\n",
    "\n",
    "## Preprocessed Data Structure"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e294352-2408-4b8f-938f-b32a3bdcffa0",
   "metadata": {},
   "source": [
    "```\n",
    "                          row[t]                                             row[t+1]\n",
    "Local\n",
    "+-------------------------------------------------------------+----------------------------\n",
    "|local_ts[t]                                                  |local_ts[t+1]\n",
    "|                                                             |\n",
    "|best_bid[t]                                                  |best_bid[t+1]\n",
    "|best_ask[t]                                                  |best_ask[t+1]\n",
    "+-------------------------------------------------------------+---------------------------\n",
    "Exchange\n",
    "+-------------------------------------------------------------+---------------------------\n",
    "|                                                bid_fill[t+1]|\n",
    "|                                                ask_fill[t+1]|\n",
    "+----------------------------+--------------------------------+---------------------------\n",
    "|  order entry latency at    |order_ack_ts[t]                 |\n",
    "|                local_ts[t] |                                |\n",
    "|                            |best_bid_ack[t]                 |\n",
    "|                            |best_ask_ack[t]                 |\n",
    "|                            |                                |\n",
    "|             bid_fill_ack[t]|           bid_fill_after_ack[t]|\n",
    "|             ask_fill_ack[t]|           ask_fill_after_ack[t]|\n",
    "+----------------------------+--------------------------------+---------------------------\n",
    "```\n",
    "\n",
    "The open order which is already acknowledged by the exchange between local_ts[t] to local_ts[t + 1] is filled by bid_fill[t + 1] or ask_fill[t + 1] based on the fill condition we describe above and the local knows it at local_ts[t + 1].\n",
    "\n",
    "If a user sends a new order at local_ts[t], this order is checked if accepted based on best_bid_ack[t] or best_ask_ack[t] (if it is GTX, or marketable order). And if the limit order is accepted, it is checked if is filled until local_ts[t + 1] by bid_fill_after_ack[t] or ask_fill_after_ack[t]. A user know it at local_ts[t + 1].\n",
    "\n",
    "If a user sends a cancel order at local_ts[t], the open order is checked if it is filled before cancel request is acknowledge by bid_fill_ack[t] or ask_fill_ack[t]. And also a user know it at local_ts[t + 1].\n",
    "\n",
    "If order entry latency is high enough that the order request arrives at exchange after local_ts[t + 1], compose the row as shown in the following. \n",
    "\n",
    "```\n",
    "Local\n",
    "+------------------------------+-------------------------------------------------------------+-------------\n",
    "|local_ts[t]                   |local_ts[t+1]                                                |local_ts[t+2]\n",
    "|                              |                                                             |\n",
    "|best_bid[t]                   |best_bid[t+1]                                                |\n",
    "|best_ask[t]                   |best_ask[t+1]                                                |\n",
    "+------------------------------+-------------------------------------------------------------+-------------\n",
    "Exchange\n",
    "+------------------------------+-------------------------------------------------------------+-------------\n",
    "|                 bid_fill[t+1]|                                                bid_fill[t+2]|\n",
    "|                 ask_fill[t+1]|                                                ask_fill[t+2]|\n",
    "+------------------------------+------------------------+------------------------------------+-------------\n",
    "|           order entry latency at local_ts[t]          |order_ack_ts[t]                     |\n",
    "|                                                       |                                    |\n",
    "|                                                       |best_bid_ack[t]                     |\n",
    "|                                                       |best_ask_ack[t]                     |\n",
    "|                                                       |                                    |\n",
    "|                                        bid_fill_ack[t]|               bid_fill_after_ack[t]|\n",
    "|                                        ask_fill_ack[t]|               ask_fill_after_ack[t]|\n",
    "+------------------------------+------------------------+---+--------------------------------+-------------\n",
    "|                              |                            |order_ack_ts[t+1]               |\n",
    "|                              |                            |                                |\n",
    "|                              |                            |best_bid_ack[t+1]               |\n",
    "|                              |                            |best_ask_ack[t+1]               |\n",
    "+------------------------------+----------------------------+--------------------------------+-------------\n",
    "|                              | order entry latency at     |order_ack_ts[t+1]               |\n",
    "|                              |              local_ts[t+1] |                                |\n",
    "|                              |                            |best_bid_ack[t+1]               |\n",
    "|                              |                            |best_ask_ack[t+1]               |\n",
    "|                              |                            |                                |\n",
    "|                              |           bid_fill_ack[t+1]|         bid_fill_after_ack[t+1]|\n",
    "|                              |           ask_fill_ack[t+1]|         ask_fill_after_ack[t+1]|\n",
    "+------------------------------+----------------------------+--------------------------------+-------------\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cac062e4-e142-455e-aaf8-e893d62b2c04",
   "metadata": {},
   "source": [
    "## Preprocessing Market Data for Accelerated Backtesting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0fc74326-b52f-4a5a-9bc7-e19aa52eb049",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import numba as nb\n",
    "from numba import njit\n",
    "from numba.experimental import jitclass\n",
    "\n",
    "INVALID_MIN = 0\n",
    "INVALID_MAX = np.iinfo(np.int64).max - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "82490f19-8b5d-41b5-b3f8-4af84744d0e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "@jitclass\n",
    "class Clock:\n",
    "    timestamp: nb.int64[:]\n",
    "    rn: nb.int64\n",
    "    ts: nb.int64\n",
    "    \n",
    "    def __init__(self, timestamp, rn):\n",
    "        self.timestamp = timestamp\n",
    "        self.rn = rn\n",
    "        if self.rn >= len(self.timestamp):\n",
    "            self.ts = INVALID_MAX\n",
    "        else:\n",
    "            self.ts = self.timestamp[self.rn]\n",
    "\n",
    "    def next(self):\n",
    "        if self.rn == len(self.timestamp) - 1:\n",
    "            self.ts = INVALID_MAX\n",
    "        else:\n",
    "            self.rn += 1\n",
    "            self.ts = self.timestamp[self.rn]\n",
    "\n",
    "@njit\n",
    "def select_event(timestamps):\n",
    "    # Finds the earliest timestamped event to process first.\n",
    "    earliest_ts = INVALID_MAX\n",
    "    ev = -1\n",
    "    for i in range(len(timestamps)):\n",
    "        if timestamps[i] < earliest_ts:\n",
    "            earliest_ts = timestamps[i]\n",
    "            ev = i\n",
    "    return ev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "19699bb2-223f-4e87-be4a-0412f61ad4f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "order_latency_dtype = np.dtype([('req_ts', 'i8'), ('exch_ts', 'i8'), ('resp_ts', 'i8'), ('_padding', 'i8')])\n",
    "\n",
    "@jitclass\n",
    "class IntpOrderLatency:\n",
    "    rn: nb.int64\n",
    "    data: nb.from_dtype(order_latency_dtype)[:]\n",
    "    \n",
    "    def __init__(self, data):\n",
    "        self.rn = 0\n",
    "        self.data = data\n",
    "        \n",
    "    def entry(self, timestamp):\n",
    "        # Returns the order entry latency, interpolated from the order entry latency before the timestamp \n",
    "        # and the order entry latency after the timestamp.\n",
    "        while (\n",
    "            self.rn < len(self.data)\n",
    "            and self.data[self.rn].req_ts < timestamp\n",
    "        ):\n",
    "            self.rn += 1\n",
    "\n",
    "        if self.rn == 0:\n",
    "            entry_latency = self.data[self.rn].exch_ts - self.data[self.rn].req_ts\n",
    "        elif self.rn == len(self.data):\n",
    "            entry_latency = self.data[self.rn - 1].exch_ts - self.data[self.rn - 1].req_ts\n",
    "        else:\n",
    "            # todo: Handle negative latency values, which indicate a technical issue \n",
    "            #       (e.g., server overload) that causes the order request to be rejected.\n",
    "            #       Please see https://docs.rs/hftbacktest/latest/hftbacktest/backtest/models/struct.IntpOrderLatency.html\n",
    "            prev_req_ts = self.data[self.rn - 1].req_ts\n",
    "            next_req_ts = self.data[self.rn].req_ts\n",
    "            prev_entry_latency = self.data[self.rn - 1].exch_ts - prev_req_ts\n",
    "            next_entry_latency = self.data[self.rn].exch_ts - next_req_ts\n",
    "            \n",
    "            entry_latency = np.divide(\n",
    "                next_entry_latency - prev_entry_latency, \n",
    "                next_req_ts - prev_req_ts\n",
    "            ) * (timestamp - prev_req_ts) + prev_entry_latency\n",
    "\n",
    "        return entry_latency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d98971dc-8b1e-4ab9-9cb0-3309568d562d",
   "metadata": {},
   "outputs": [],
   "source": [
    "@njit\n",
    "def ack_order(\n",
    "    tick_size,\n",
    "    order_ack_ts,\n",
    "    next_local_ts,\n",
    "    book_ticker_rn,\n",
    "    book_ticker_exch_ts,\n",
    "    book_ticker,\n",
    "    trades_rn,\n",
    "    trades_exch_ts,\n",
    "    trades\n",
    "):\n",
    "    # This function finds the fill prices around order_ack_ts, as well as the best bid and best ask at order_ack_ts.\n",
    "    # - Fill prices between local_ts[t] and order_ack_ts[t]\n",
    "    # - Fill prices between order_ack_ts[t] and local_ts[t + n], where local_ts[t + n] > order_ack_ts[t]\n",
    "    \n",
    "    # Initializes the values from the last best bid and best ask.\n",
    "    best_bid_tick = round(book_ticker[book_ticker_rn - 1].bid_px / tick_size)\n",
    "    ask_fill_tick = ask_fill_tick_ack = best_bid_tick_ack = high_best_bid_tick = best_bid_tick\n",
    "    best_ask_tick = round(book_ticker[book_ticker_rn - 1].ask_px / tick_size)\n",
    "    bid_fill_tick = bid_fill_tick_ack = best_ask_tick_ack = low_best_ask_tick = best_ask_tick\n",
    "    \n",
    "    high_buy_tick = INVALID_MIN\n",
    "    low_sell_tick = INVALID_MAX\n",
    "\n",
    "    book_ticker_exch_clock = Clock(book_ticker_exch_ts, book_ticker_rn)\n",
    "    trades_exch_clock = Clock(trades_exch_ts, trades_rn)\n",
    "    \n",
    "    while True:\n",
    "        ev = select_event(np.asarray([\n",
    "            book_ticker_exch_clock.ts,\n",
    "            trades_exch_clock.ts,\n",
    "            order_ack_ts,\n",
    "            next_local_ts\n",
    "        ]))\n",
    "\n",
    "        if ev == -1:\n",
    "            raise ValueError\n",
    "        elif ev == 0:\n",
    "            best_bid_tick = round(book_ticker[book_ticker_exch_clock.rn].bid_px / tick_size)\n",
    "            best_ask_tick = round(book_ticker[book_ticker_exch_clock.rn].ask_px / tick_size)\n",
    "\n",
    "            if best_bid_tick > high_best_bid_tick:\n",
    "                high_best_bid_tick = best_bid_tick\n",
    "            if best_ask_tick < low_best_ask_tick:\n",
    "                low_best_ask_tick = best_ask_tick\n",
    "                \n",
    "            book_ticker_exch_clock.next()\n",
    "        elif ev == 1:\n",
    "            side = trades[trades_exch_clock.rn].side\n",
    "            px_tick = round(trades[trades_exch_clock.rn].px / tick_size)\n",
    "            \n",
    "            if side == 1 and px_tick > high_buy_tick:\n",
    "                high_buy_tick = px_tick\n",
    "            elif side == -1 and px_tick < low_sell_tick:\n",
    "                low_sell_tick = px_tick\n",
    "\n",
    "            trades_exch_clock.next()\n",
    "        elif ev == 2:\n",
    "            # An order request is acknowledged by the exchange at order_ack_ts[t].\n",
    "            bid_fill_tick_ack = min(low_sell_tick + 1, low_best_ask_tick)\n",
    "            ask_fill_tick_ack = max(high_buy_tick - 1, high_best_bid_tick)\n",
    "            best_bid_tick_ack = best_bid_tick\n",
    "            best_ask_tick_ack = best_ask_tick\n",
    "\n",
    "            high_buy_tick = INVALID_MIN\n",
    "            high_best_bid_tick = best_bid_tick\n",
    "            low_sell_tick = INVALID_MAX\n",
    "            low_best_ask_tick = best_ask_tick\n",
    "            \n",
    "            order_ack_ts = INVALID_MAX\n",
    "        elif ev == 3:\n",
    "            # at local_ts[t + n] > order_ack_ts[t]\n",
    "            bid_fill_tick = min(low_sell_tick + 1, low_best_ask_tick)\n",
    "            ask_fill_tick = max(high_buy_tick - 1, high_best_bid_tick)\n",
    "            break\n",
    "            \n",
    "    return (\n",
    "        bid_fill_tick_ack,\n",
    "        ask_fill_tick_ack,\n",
    "        best_bid_tick_ack,\n",
    "        best_ask_tick_ack,\n",
    "        bid_fill_tick,\n",
    "        ask_fill_tick\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "13b37f2b-904d-4242-8a91-b2c1d97ed80b",
   "metadata": {},
   "outputs": [],
   "source": [
    "@njit\n",
    "def preprocess_data(\n",
    "    tick_size,\n",
    "    end_ts,\n",
    "    local_ts,\n",
    "    book_ticker_exch_ts,\n",
    "    book_ticker_local_ts,\n",
    "    book_ticker,\n",
    "    trades_exch_ts,\n",
    "    trades,\n",
    "    order_latency\n",
    "):\n",
    "    # Preprocessed data\n",
    "    # All prices are in ticks to avoid additional operations to prevent floating-point comparison errors.\n",
    "    out_t = 0\n",
    "    out_size = len(local_ts)\n",
    "    # timestamp at local := local_ts[t]\n",
    "    out_local_ts = np.empty(out_size, np.int64)\n",
    "    # best bid at local at local_ts[t]\n",
    "    out_best_bid_tick = np.empty(out_size, np.int64)\n",
    "    # best ask at local at local_ts[t]\n",
    "    out_best_ask_tick = np.empty(out_size, np.int64)\n",
    "    # bid fill price in ticks for the interval local_ts[t - 1] ~ local_ts[t]\n",
    "    # any open buy orders during this interval with a price greater than or equal to this price are considered filled.\n",
    "    out_bid_fill_tick = np.empty(out_size, np.int64)\n",
    "    # ask fill price in ticks for the interval local_ts[t - 1] ~ local_ts[t]\n",
    "    # any open sell orders during this interval with a price less than or equal to this price are considered filled.\n",
    "    out_ask_fill_tick = np.empty(out_size, np.int64)\n",
    "    # order acknowledgment timestamp at the exchange, when an order is sent at local_ts[t], is defined as order_ack_ts[t].\n",
    "    out_order_ack_ts = np.empty(out_size, np.int64)\n",
    "    # bid fill price in ticks for the interval local_ts[t] ~ order_ack_ts[t]\n",
    "    # any open buy orders during this interval with a price greater than or equal to this price are considered filled.\n",
    "    out_bid_fill_tick_ack = np.empty(out_size, np.int64)\n",
    "    # ask fill price in ticks for the interval local_ts[t] ~ order_ack_ts[t]\n",
    "    # any open sell orders during this interval with a price less than or equal to this price are considered filled.\n",
    "    out_ask_fill_tick_ack = np.empty(out_size, np.int64)\n",
    "    # best bid at the exchange at order_ack_ts[t]\n",
    "    # used to determine whether the order should be accepted (limit or market) or rejected (GTX).\n",
    "    out_best_bid_tick_ack = np.empty(out_size, np.int64)\n",
    "    # best ask at the exchange at order_ack_ts[t]\n",
    "    # used to determine whether the order should be accepted (limit or market) or rejected (GTX).\n",
    "    out_best_ask_tick_ack = np.empty(out_size, np.int64)\n",
    "    # bid fill price in ticks for the interval order_ack_ts[t] ~ local_ts[t + n] where local_ts[t + n] > order_ack_ts[t].\n",
    "    # any open buy orders during this interval with a price greater than or equal to this price are considered filled.\n",
    "    out_bid_fill_tick_after_ack = np.empty(out_size, np.int64)\n",
    "    # ask fill price in ticks for the interval order_ack_ts[t] ~ local_ts[t + n] where local_ts[t + n] > order_ack_ts[t].\n",
    "    # any open sell orders during this interval with a price less than or equal to this price are considered filled.\n",
    "    out_ask_fill_tick_after_ack = np.empty(out_size, np.int64)\n",
    "    \n",
    "    local_best_bid_tick = exch_best_bid_tick = INVALID_MIN\n",
    "    local_best_ask_tick = exch_best_ask_tick = INVALID_MAX\n",
    "    high_buy_tick = high_best_bid_tick = INVALID_MIN\n",
    "    low_sell_tick = low_best_ask_tick = INVALID_MAX\n",
    "\n",
    "    # Initializes the clocks\n",
    "    # todo: For better accuracy, it also needs to combine the best bid and ask from Level-2 market depth data\n",
    "    #       with the best bid and ask from the book ticker.\n",
    "    book_ticker_exch_clock = Clock(book_ticker_exch_ts, 0)\n",
    "    book_ticker_local_clock = Clock(book_ticker_local_ts, 0)\n",
    "    trades_exch_clock = Clock(trades_exch_ts, 0)\n",
    "    local_clock = Clock(local_ts, 0)\n",
    "\n",
    "    order_latency_rn = 0\n",
    "\n",
    "    while local_clock.ts <= end_ts:\n",
    "        # Selects the event to process.\n",
    "        ev = select_event(np.asarray([\n",
    "            book_ticker_exch_clock.ts,\n",
    "            book_ticker_local_clock.ts,\n",
    "            trades_exch_clock.ts,\n",
    "            local_clock.ts\n",
    "        ]))\n",
    "\n",
    "        if ev == -1:\n",
    "            # Should not happen.\n",
    "            raise ValueError\n",
    "        elif ev == 0:\n",
    "            # Updates the current exchange best bid and best ask.\n",
    "            exch_best_bid_tick = round(book_ticker[book_ticker_exch_clock.rn].bid_px / tick_size)\n",
    "            exch_best_ask_tick = round(book_ticker[book_ticker_exch_clock.rn].ask_px / tick_size)\n",
    "\n",
    "            # Updates the highest and lowest best bid and best ask at the exchange.\n",
    "            if exch_best_bid_tick > high_best_bid_tick:\n",
    "                high_best_bid_tick = exch_best_bid_tick\n",
    "            if exch_best_ask_tick < low_best_ask_tick:\n",
    "                low_best_ask_tick = exch_best_ask_tick\n",
    "\n",
    "            book_ticker_exch_clock.next()\n",
    "        elif ev == 1:\n",
    "            # Updates the current local best bid and best ask.\n",
    "            local_best_bid_tick = round(book_ticker[book_ticker_local_clock.rn].bid_px / tick_size)\n",
    "            local_best_ask_tick = round(book_ticker[book_ticker_local_clock.rn].ask_px / tick_size)\n",
    "    \n",
    "            book_ticker_local_clock.next()\n",
    "        elif ev == 2:\n",
    "            side = trades[trades_exch_clock.rn].side\n",
    "            px_tick = round(trades[trades_exch_clock.rn].px / tick_size)\n",
    "\n",
    "            # Updates the highest and lowest trade at the exchange.\n",
    "            if side == 1 and px_tick > high_buy_tick:\n",
    "                high_buy_tick = px_tick\n",
    "            elif side == -1 and px_tick < low_sell_tick:\n",
    "                low_sell_tick = px_tick\n",
    "\n",
    "            trades_exch_clock.next()\n",
    "        elif ev == 3:\n",
    "            # Records the fill prices in ticks at the exchange between local_ts[t - 1] and local_ts[t].\n",
    "            out_bid_fill_tick[out_t] = min(low_sell_tick + 1, low_best_ask_tick)\n",
    "            out_ask_fill_tick[out_t] = max(high_buy_tick - 1, high_best_bid_tick)\n",
    "\n",
    "            high_buy_tick = INVALID_MIN\n",
    "            high_best_bid_tick = exch_best_bid_tick\n",
    "            low_sell_tick = INVALID_MAX\n",
    "            low_best_ask_tick = exch_best_ask_tick\n",
    "            \n",
    "            # Records the current local state at local_ts[t].\n",
    "            out_local_ts[out_t] = local_clock.ts\n",
    "            out_best_bid_tick[out_t] = local_best_bid_tick\n",
    "            out_best_ask_tick[out_t] = local_best_ask_tick\n",
    "\n",
    "            # Order acknowledgement timestamp when the exchange receives the order request.\n",
    "            order_entry_latency = order_latency.entry(local_clock.ts)\n",
    "            order_ack_ts = local_clock.ts + order_entry_latency\n",
    "            \n",
    "            # The next local timestamp after the exchange acknowledges the order request.\n",
    "            next_local_clock = Clock(local_ts, local_clock.rn)\n",
    "            while next_local_clock.ts < order_ack_ts:\n",
    "                next_local_clock.next()\n",
    "            next_local_ts = next_local_clock.ts\n",
    "\n",
    "            # Computes the fill prices around the order acknowledgment timestamp at the exchange\n",
    "            # and finds the best bid and best ask at the time of acknowledgment.\n",
    "            (\n",
    "                bid_fill_tick_ack,\n",
    "                ask_fill_tick_ack,\n",
    "                best_bid_tick_ack,\n",
    "                best_ask_tick_ack,\n",
    "                bid_fill_tick,\n",
    "                ask_fill_tick\n",
    "            ) = ack_order(\n",
    "                tick_size,\n",
    "                order_ack_ts,\n",
    "                next_local_ts,\n",
    "                book_ticker_exch_clock.rn + 1,\n",
    "                book_ticker_exch_ts,\n",
    "                book_ticker,\n",
    "                trades_exch_clock.rn + 1,\n",
    "                trades_exch_ts,\n",
    "                trades\n",
    "            )\n",
    "                \n",
    "            # Records the values related to the order acknowledgment.\n",
    "            out_order_ack_ts[out_t] = order_ack_ts\n",
    "            out_bid_fill_tick_ack[out_t] = bid_fill_tick_ack\n",
    "            out_ask_fill_tick_ack[out_t] = ask_fill_tick_ack\n",
    "            out_best_bid_tick_ack[out_t] = best_bid_tick_ack\n",
    "            out_best_ask_tick_ack[out_t] = best_ask_tick_ack\n",
    "            out_bid_fill_tick_after_ack[out_t] = bid_fill_tick\n",
    "            out_ask_fill_tick_after_ack[out_t] = ask_fill_tick\n",
    "        \n",
    "            out_t += 1\n",
    "\n",
    "            local_clock.next()\n",
    "            \n",
    "    return (\n",
    "        out_local_ts[:out_t],\n",
    "        out_best_bid_tick[:out_t],\n",
    "        out_best_ask_tick[:out_t],\n",
    "        out_bid_fill_tick[:out_t],\n",
    "        out_ask_fill_tick[:out_t],\n",
    "        out_order_ack_ts[:out_t],\n",
    "        out_bid_fill_tick_ack[:out_t],\n",
    "        out_ask_fill_tick_ack[:out_t],\n",
    "        out_best_bid_tick_ack[:out_t],\n",
    "        out_best_ask_tick_ack[:out_t],\n",
    "        out_bid_fill_tick_after_ack[:out_t],\n",
    "        out_ask_fill_tick_after_ack[:out_t]\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "679948fc-af0c-4403-9fed-aa9d44875f7d",
   "metadata": {},
   "source": [
    "## Preprocessing Example\n",
    "\n",
    "Tardis.dev provides free sample data for the first day of each month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6903945e-6f76-473a-802d-d133cd883ca3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !curl -L -o BTCUSDT_incremental_book_L2_20250801.csv.gz https://datasets.tardis.dev/v1/binance-futures/incremental_book_L2/2025/08/01/BTCUSDT.csv.gz\n",
    "# !curl -L -o BTCUSDT_trades_20250801.csv.gz https://datasets.tardis.dev/v1/binance-futures/trades/2025/08/01/BTCUSDT.csv.gz\n",
    "# !curl -L -o BTCUSDT_book_ticker_20250801.csv.gz https://datasets.tardis.dev/v1/binance-futures/book_ticker/2025/08/01/BTCUSDT.csv.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f9b0e047-24c6-4fb9-b2ae-acea9cf608f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import polars as pl\n",
    "\n",
    "def load_book_ticker(file):\n",
    "    df = pl.read_csv(file)\n",
    "    exch_ts = df['timestamp'].to_numpy() * 1000\n",
    "    local_ts = df['local_timestamp'].to_numpy() * 1000\n",
    "    data = df.select(\n",
    "        pl.col('ask_amount').alias('ask_qty'),\n",
    "        pl.col('ask_price').alias('ask_px'),\n",
    "        pl.col('bid_price').alias('bid_px'),\n",
    "        pl.col('bid_amount').alias('bid_qty'),\n",
    "    ).to_numpy(structured=True)\n",
    "    return exch_ts, local_ts, data\n",
    "\n",
    "def load_trades(file):\n",
    "    df = pl.read_csv(file)\n",
    "    exch_ts = df['timestamp'].to_numpy() * 1000\n",
    "    local_ts = df['local_timestamp'].to_numpy() * 1000\n",
    "    data = df.select(\n",
    "        pl.when(pl.col('side') == 'buy').then(1).otherwise(-1).alias('side'),\n",
    "        pl.col('price').alias('px'),\n",
    "        pl.col('amount').alias('qty'),\n",
    "    ).to_numpy(structured=True)\n",
    "    return exch_ts, local_ts, data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7a67eb2a-caf2-4627-b5af-93bb28dbea86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correcting the latency\n",
      "Correcting the event order\n",
      "Saving to BTCUSDT_20250801.npz\n"
     ]
    }
   ],
   "source": [
    "# For demonstration purposes, order latency artificially derived from feed latency is used.\n",
    "# For more realistic backtesting, actual order latency data should be used.\n",
    "# Please see https://hftbacktest.readthedocs.io/en/latest/tutorials/Order%20Latency%20Data.html\n",
    "\n",
    "from hftbacktest.data.utils.tardis import convert_fuse\n",
    "from hftbacktest.data.utils import feed_order_latency\n",
    "\n",
    "# First, we convert the Tardis data into the normalized format to use the utility \n",
    "# that generates order latency from feed latency.\n",
    "# This normalized data will also be used to compare accelerated backtesting \n",
    "# (which removes certain conditions) against full backtesting (which includes all conditions).\n",
    "convert_fuse(\n",
    "    'BTCUSDT_trades_20250801.csv.gz', \n",
    "    'BTCUSDT_incremental_book_L2_20250801.csv.gz',\n",
    "    'BTCUSDT_book_ticker_20250801.csv.gz',\n",
    "    tick_size=0.1,\n",
    "    lot_size=0.001,\n",
    "    output_filename='BTCUSDT_20250801.npz'\n",
    ")\n",
    "\n",
    "# Generates order latency from feed latency.\n",
    "# At any given time:\n",
    "#   - Order entry latency = 4 × feed latency\n",
    "#   - Order response latency = 3 × feed latency\n",
    "# Please see the document for details about the arguments.\n",
    "feed_order_latency.generate_order_latency(\n",
    "    'BTCUSDT_20250801.npz',\n",
    "    output_file='feed_order_latency_20250801.npz',\n",
    "    mul_entry=4,\n",
    "    mul_resp=3\n",
    ")\n",
    "\n",
    "order_latency_data = np.load('feed_order_latency_20250801.npz')['data']\n",
    "order_latency = IntpOrderLatency(order_latency_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "23a9f1e4-1614-4801-87d8-913f3c2af90f",
   "metadata": {},
   "outputs": [],
   "source": [
    "tick_size = 0.1\n",
    "lot_size = 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5372826c-0f15-4130-8131-d1486903a5dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "\n",
    "# 0.1 seconds\n",
    "running_interval = 100_000_000\n",
    "start_ts = int(datetime.datetime(2025, 8, 1, tzinfo=datetime.timezone.utc).timestamp() * 1_000_000_000) + running_interval\n",
    "end_ts = int(datetime.datetime(2025, 8, 2, tzinfo=datetime.timezone.utc).timestamp() * 1_000_000_000)\n",
    "\n",
    "# In the final interval, to compute fill prices after order acknowledgment,\n",
    "# a small buffer beyond `end_ts` is necessary.\n",
    "local_ts = np.arange(start_ts, end_ts + 100 * running_interval, running_interval)\n",
    "\n",
    "# Additionally, the following day’s data should be concatenated to compute accurate fill prices for the final interval.\n",
    "def concat(a, b):\n",
    "    ret = []\n",
    "    for aa, bb in zip(a, b):\n",
    "        ret.append(np.concatenate([aa, bb], axis=0))\n",
    "    return ret\n",
    "    \n",
    "# book_ticker = concat(\n",
    "#     load_book_ticker('BTCUSDT_book_ticker_20250501.csv.gz'),\n",
    "#     load_book_ticker('BTCUSDT_book_ticker_20250502.csv.gz')\n",
    "# )\n",
    "# trades = concat(\n",
    "#     load_trades('BTCUSDT_trades_20250501.csv.gz'),\n",
    "#     load_trades('BTCUSDT_trades_20250502.csv.gz')\n",
    "# )\n",
    "\n",
    "# For demonstration purposes, a single day of data is used.\n",
    "book_ticker = load_book_ticker('BTCUSDT_book_ticker_20250801.csv.gz')\n",
    "trades = load_trades('BTCUSDT_trades_20250801.csv.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "369797d5-9989-4791-bb7b-8730d38e5d75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9.12 s, sys: 81.2 ms, total: 9.2 s\n",
      "Wall time: 9.17 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "out = preprocess_data(\n",
    "    tick_size,\n",
    "    end_ts,\n",
    "    local_ts,\n",
    "    book_ticker[0],\n",
    "    book_ticker[1],\n",
    "    book_ticker[2],\n",
    "    trades[0],\n",
    "    trades[2],\n",
    "    order_latency\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ec446951-eb02-4e24-9223-d7b37ae964df",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyarrow as pa\n",
    "import pyarrow.parquet as pq\n",
    "\n",
    "# Saves the preprocessed data to a Parquet file.\n",
    "table = pa.table({\n",
    "    'local_ts': out[0],\n",
    "    'best_bid_tick': out[1],\n",
    "    'best_ask_tick': out[2],\n",
    "    'bid_fill_tick': out[3],\n",
    "    'ask_fill_tick': out[4],\n",
    "    'order_ack_ts': out[5],\n",
    "    'bid_fill_tick_ack': out[6],\n",
    "    'ask_fill_tick_ack': out[7],\n",
    "    'best_bid_tick_ack': out[8],\n",
    "    'best_ask_tick_ack': out[9],\n",
    "    'bid_fill_tick_after_ack': out[10],\n",
    "    'ask_fill_tick_after_ack': out[11]\n",
    "})\n",
    "\n",
    "pq.write_table(table, 'BTCUSDT_20250801.parquet', compression='zstd')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2efbc116-a27d-4e42-9d04-12e4d6dd2f71",
   "metadata": {},
   "source": [
    "## Accelerated Backtesting Using Preprocessed Market Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b94d1ee3-72a7-423e-b702-190bd91476bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hftbacktest.types import record_dtype\n",
    "\n",
    "@njit\n",
    "def accelerated_backtest(\n",
    "    relative_half_spread,\n",
    "    skew,\n",
    "    order_notional_value,\n",
    "    max_notional_position,\n",
    "    fee,\n",
    "    tick_size,\n",
    "    lot_size,\n",
    "    local_ts,\n",
    "    best_bid_tick,\n",
    "    best_ask_tick,\n",
    "    bid_fill_tick,\n",
    "    ask_fill_tick,\n",
    "    order_ack_ts,\n",
    "    bid_fill_tick_ack,\n",
    "    ask_fill_tick_ack,\n",
    "    best_bid_tick_ack,\n",
    "    best_ask_tick_ack,\n",
    "    bid_fill_tick_after_ack,\n",
    "    ask_fill_tick_after_ack\n",
    "):\n",
    "    # req_bid_tick: bid order price in ticks (limit buy order with GTX) sent to the exchange, before the exchange acknowledges it.\n",
    "    # req_ask_tick: ask order price in ticks (limit sell order with GTX) sent to the exchange, before the exchange acknowledges it.\n",
    "    # open_bid_tick: bid order price in ticks acknowledged by the exchange, currently an open order in the market.\n",
    "    # open_ask_tick: ask order price in ticks acknowledged by the exchange, currently an open order in the market.\n",
    "    #\n",
    "    # INVALID_MIN and INVALID_MAX indicate that there are no orders.\n",
    "    #\n",
    "    # Example:\n",
    "    #   If req_bid_tick is INVALID_MIN and there is an open bid order, \n",
    "    #   the open bid order will be canceled (if the cancel request reaches the exchange before the order is filled).\n",
    "    #   When an order is filled, its price is set to INVALID_MIN or INVALID_MAX accordingly.\n",
    "    req_bid_tick = open_bid_tick = INVALID_MIN\n",
    "    req_ask_tick = open_ask_tick = INVALID_MAX\n",
    "    # corresponding order quantities.\n",
    "    open_bid_qty = req_bid_qty = 0.0\n",
    "    open_ask_qty = req_ask_qty = 0.0\n",
    "\n",
    "    # Initial state.\n",
    "    balance = 0.0\n",
    "    position = 0.0\n",
    "    num_trades = 0\n",
    "    trading_value = 0.0\n",
    "    trading_volume = 0.0\n",
    "\n",
    "    # Row index iterator\n",
    "    t = 0\n",
    "\n",
    "    # State record for stats\n",
    "    rec_i = 0\n",
    "    record = np.empty(len(local_ts), record_dtype)\n",
    "    \n",
    "    while True:\n",
    "        #--------------------------------------------------------\n",
    "        # Local bot logic at `local_ts[t]`.\n",
    "        mid_tick = (best_bid_tick[t] + best_ask_tick[t]) / 2.0\n",
    "        mid_px = mid_tick * tick_size\n",
    "        \n",
    "        notional_position_value = position * mid_px\n",
    "        normalized_position = notional_position_value / max_notional_position\n",
    "\n",
    "        relative_bid_depth = relative_half_spread + skew * normalized_position\n",
    "        relative_ask_depth = relative_half_spread - skew * normalized_position\n",
    "\n",
    "        req_bid_tick = min(np.floor(mid_tick * (1.0 - relative_bid_depth)), best_bid_tick[t])\n",
    "        req_ask_tick = max(np.ceil(mid_tick * (1.0 + relative_ask_depth)), best_ask_tick[t])\n",
    "\n",
    "        req_bid_qty = req_ask_qty = max(round(order_notional_value / mid_px / lot_size) * lot_size, lot_size)\n",
    "        \n",
    "        # If the position exceeds the risk limit (max notional position),\n",
    "        # no orders shall be open in that direction.\n",
    "        if normalized_position > 1:\n",
    "            req_bid_tick = INVALID_MIN\n",
    "        if normalized_position < -1:\n",
    "            req_ask_tick = INVALID_MAX\n",
    "\n",
    "        #--------------------------------------------------------\n",
    "        # Records the current state.\n",
    "        record[rec_i].timestamp = local_ts[t]\n",
    "        record[rec_i].price = mid_tick * tick_size\n",
    "        record[rec_i].position = position\n",
    "        record[rec_i].balance = balance * tick_size\n",
    "        record[rec_i].fee = trading_value * tick_size * fee\n",
    "        record[rec_i].num_trades = num_trades\n",
    "        record[rec_i].trading_volume = trading_volume\n",
    "        record[rec_i].trading_value = trading_value * tick_size\n",
    "        \n",
    "        rec_i += 1\n",
    "\n",
    "        #--------------------------------------------------------\n",
    "        # Processes the exchange-side logic (order fill logic).\n",
    "        \n",
    "        # If any of the requested order prices differ from the open order's price,\n",
    "        # it is assumed that the bot sent the order request.\n",
    "        # The request will be acknowledged and processed at `order_ack_ts[t]`.\n",
    "        # Otherwise, check if the open order is filled.\n",
    "        if req_bid_tick != open_bid_tick or req_ask_tick != open_ask_tick:\n",
    "            # The current time is `order_ack_ts[t]`.\n",
    "            order_ack_ts_ = order_ack_ts[t]\n",
    "\n",
    "            # If there are open orders with valid prices,\n",
    "            # checks whether they are filled before accepting the newly requested orders.\n",
    "            if open_bid_tick > INVALID_MIN and open_bid_tick >= bid_fill_tick_ack[t]:\n",
    "                execute_value = open_bid_tick * open_bid_qty\n",
    "                balance -= execute_value\n",
    "                position += open_bid_qty\n",
    "                num_trades += 1\n",
    "                trading_volume += open_bid_qty\n",
    "                trading_value += execute_value\n",
    "                # Invalidates the price because the order is filled.\n",
    "                open_bid_tick = INVALID_MIN\n",
    "            if open_ask_tick < INVALID_MAX and open_ask_tick <= ask_fill_tick_ack[t]:\n",
    "                execute_value = open_ask_tick * open_ask_qty\n",
    "                balance += execute_value\n",
    "                position -= open_ask_qty\n",
    "                num_trades += 1\n",
    "                trading_volume += open_ask_qty\n",
    "                trading_value += execute_value\n",
    "                # Invalidates the price because the order is filled.\n",
    "                open_ask_tick = INVALID_MAX\n",
    "            \n",
    "            # New orders are treated as GTX. \n",
    "            # If the requested buy order price is greater than or equal to the best ask,\n",
    "            # or the requested sell order price is less than or equal to the best bid, \n",
    "            # the orders are rejected.\n",
    "            # Invalidates the price if the order is rejected.\n",
    "            if req_bid_tick >= best_ask_tick_ack[t]:\n",
    "                req_bid_tick = INVALID_MIN\n",
    "            if req_ask_tick <= best_bid_tick_ack[t]:\n",
    "                req_ask_tick = INVALID_MAX\n",
    "\n",
    "            # Updates the open orders to reflect accepted orders.\n",
    "            open_bid_tick = req_bid_tick\n",
    "            open_ask_tick = req_ask_tick\n",
    "            open_bid_qty = req_bid_qty\n",
    "            open_ask_qty = req_ask_qty\n",
    "\n",
    "            # If there are open orders with valid prices,\n",
    "            # checks whether they are filled before the next local timestamp (`local_ts[t+n]`)\n",
    "            # that is greater than the current timestamp (`order_ack_ts[t]`).\n",
    "            if open_bid_tick > INVALID_MIN and open_bid_tick >= bid_fill_tick_after_ack[t]:\n",
    "                execute_value = open_bid_tick * open_bid_qty\n",
    "                balance -= execute_value\n",
    "                position += open_bid_qty\n",
    "                num_trades += 1\n",
    "                trading_volume += open_bid_qty\n",
    "                trading_value += execute_value\n",
    "                # Invalidates the price because the order is filled.\n",
    "                open_bid_tick = INVALID_MIN\n",
    "            if open_ask_tick < INVALID_MAX and open_ask_tick <= ask_fill_tick_after_ack[t]:\n",
    "                execute_value = open_ask_tick * open_ask_qty\n",
    "                balance += execute_value\n",
    "                position -= open_ask_qty\n",
    "                num_trades += 1\n",
    "                trading_volume += open_ask_qty\n",
    "                trading_value += execute_value\n",
    "                # Invalidates the price because the order is filled.\n",
    "                open_ask_tick = INVALID_MAX\n",
    "\n",
    "            # The next local timestamp must be greater than the current timestamp (`order_ack_ts[t]`).\n",
    "            while t < len(local_ts) and local_ts[t] < order_ack_ts_:\n",
    "                t += 1\n",
    "            # Breaks if no more rows remain for processing.\n",
    "            if t == len(local_ts):\n",
    "                break\n",
    "        else:\n",
    "            # Checks if the open orders are filled between two local timestamps.\n",
    "            # The next row of data contains the bid fill price (in ticks) and ask fill price (in ticks) \n",
    "            # for that interval (step).\n",
    "            t += 1\n",
    "            # Breaks if no more rows remain for processing.\n",
    "            if t == len(local_ts):\n",
    "                break\n",
    "\n",
    "            # # If there are open orders with valid prices, checks if they are filled.\n",
    "            if open_bid_tick > INVALID_MIN and open_bid_tick >= bid_fill_tick[t]:\n",
    "                execute_value = open_bid_tick * open_bid_qty\n",
    "                balance -= execute_value\n",
    "                position += open_bid_qty\n",
    "                num_trades += 1\n",
    "                trading_volume += open_bid_qty\n",
    "                trading_value += execute_value\n",
    "                # Invalidates the price because the order is filled.\n",
    "                open_bid_tick = INVALID_MIN\n",
    "            if open_ask_tick < INVALID_MAX and open_ask_tick <= ask_fill_tick[t]:\n",
    "                execute_value = open_ask_tick * open_ask_qty\n",
    "                balance += execute_value\n",
    "                position -= open_ask_qty\n",
    "                num_trades += 1\n",
    "                trading_volume += open_ask_qty\n",
    "                trading_value += execute_value\n",
    "                # Invalidates the price because the order is filled.\n",
    "                open_ask_tick = INVALID_MAX\n",
    "        \n",
    "    return record[:rec_i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9ceba2fd-236f-411f-bc7e-69eefacdc30e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 541 ms, sys: 95.8 ms, total: 637 ms\n",
      "Wall time: 560 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "table = pq.read_table('BTCUSDT_20250801.parquet')\n",
    "\n",
    "local_ts = table['local_ts'].to_numpy()\n",
    "best_bid_tick = table['best_bid_tick'].to_numpy()\n",
    "best_ask_tick = table['best_ask_tick'].to_numpy()\n",
    "bid_fill_tick = table['bid_fill_tick'].to_numpy()\n",
    "ask_fill_tick = table['ask_fill_tick'].to_numpy()\n",
    "order_ack_ts = table['order_ack_ts'].to_numpy()\n",
    "bid_fill_tick_ack = table['bid_fill_tick_ack'].to_numpy()\n",
    "ask_fill_tick_ack = table['ask_fill_tick_ack'].to_numpy()\n",
    "best_bid_tick_ack = table['best_bid_tick_ack'].to_numpy()\n",
    "best_ask_tick_ack = table['best_ask_tick_ack'].to_numpy()\n",
    "bid_fill_tick_after_ack = table['bid_fill_tick_after_ack'].to_numpy()\n",
    "ask_fill_tick_after_ack = table['ask_fill_tick_after_ack'].to_numpy()\n",
    "\n",
    "relative_half_spread = 0.00025\n",
    "skew = relative_half_spread\n",
    "order_notional_value = 50000\n",
    "max_notional_position = order_notional_value * 20\n",
    "fee_per_value = -0.00005 # 0.005% rebates\n",
    "\n",
    "record = accelerated_backtest(\n",
    "        relative_half_spread,\n",
    "        skew,\n",
    "        order_notional_value,\n",
    "        max_notional_position,\n",
    "        fee_per_value,\n",
    "        tick_size,\n",
    "        lot_size,\n",
    "        local_ts,\n",
    "        best_bid_tick,\n",
    "        best_ask_tick,\n",
    "        bid_fill_tick,\n",
    "        ask_fill_tick,\n",
    "        order_ack_ts,\n",
    "        bid_fill_tick_ack,\n",
    "        ask_fill_tick_ack,\n",
    "        best_bid_tick_ack,\n",
    "        best_ask_tick_ack,\n",
    "        bid_fill_tick_after_ack,\n",
    "        ask_fill_tick_after_ack\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "63b9aa1f-3797-4014-a764-288e909bdfa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (1, 11)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>start</th><th>end</th><th>SR</th><th>Sortino</th><th>Return</th><th>MaxDrawdown</th><th>DailyNumberOfTrades</th><th>DailyTurnover</th><th>ReturnOverMDD</th><th>ReturnOverTrade</th><th>MaxPositionValue</th></tr><tr><td>datetime[μs]</td><td>datetime[μs]</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2025-08-01 00:00:00</td><td>2025-08-02 00:00:00</td><td>7.60934</td><td>10.894142</td><td>0.001196</td><td>0.003273</td><td>350.0</td><td>17.49908</td><td>0.365565</td><td>0.000068</td><td>347732.5629</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (1, 11)\n",
       "┌────────────┬───────────┬─────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
       "│ start      ┆ end       ┆ SR      ┆ Sortino   ┆ … ┆ DailyTurn ┆ ReturnOve ┆ ReturnOve ┆ MaxPositi │\n",
       "│ ---        ┆ ---       ┆ ---     ┆ ---       ┆   ┆ over      ┆ rMDD      ┆ rTrade    ┆ onValue   │\n",
       "│ datetime[μ ┆ datetime[ ┆ f64     ┆ f64       ┆   ┆ ---       ┆ ---       ┆ ---       ┆ ---       │\n",
       "│ s]         ┆ μs]       ┆         ┆           ┆   ┆ f64       ┆ f64       ┆ f64       ┆ f64       │\n",
       "╞════════════╪═══════════╪═════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
       "│ 2025-08-01 ┆ 2025-08-0 ┆ 7.60934 ┆ 10.894142 ┆ … ┆ 17.49908  ┆ 0.365565  ┆ 0.000068  ┆ 347732.56 │\n",
       "│ 00:00:00   ┆ 2         ┆         ┆           ┆   ┆           ┆           ┆           ┆ 29        │\n",
       "│            ┆ 00:00:00  ┆         ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "└────────────┴───────────┴─────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from hftbacktest.stats import LinearAssetRecord\n",
    "\n",
    "stats = (\n",
    "    LinearAssetRecord(record)\n",
    "        .resample('1s')\n",
    "        .stats(book_size=max_notional_position)\n",
    ")\n",
    "stats.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "213819c9-f38d-4b11-973d-e69126bf5b05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 4 Axes>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d45dd28-8796-4178-9a5e-3cbc330137f7",
   "metadata": {},
   "source": [
    "## Comparison of Backtesting Results with Full Backtesting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2699b9aa-3e93-42fd-bdc9-64ea7801b2a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from numba import uint64\n",
    "from numba.typed import Dict\n",
    "from hftbacktest import (\n",
    "    GTX,\n",
    "    LIMIT,\n",
    "    BUY,\n",
    "    SELL,\n",
    "    Recorder,\n",
    "    BacktestAsset,\n",
    "    ROIVectorMarketDepthBacktest\n",
    ")\n",
    "from hftbacktest.stats import LinearAssetRecord\n",
    "\n",
    "@njit\n",
    "def basic_mm(\n",
    "    hbt,\n",
    "    stat,\n",
    "    relative_half_spread,\n",
    "    skew,\n",
    "    interval,\n",
    "    order_notional_value,\n",
    "    max_notional_position,\n",
    "    grid_num,\n",
    "    grid_interval_tick\n",
    "):\n",
    "    asset_no = 0\n",
    "\n",
    "    tick_size = hbt.depth(0).tick_size\n",
    "    lot_size = hbt.depth(0).lot_size\n",
    "    \n",
    "    while hbt.elapse(interval) == 0:\n",
    "        hbt.clear_inactive_orders(asset_no)\n",
    "\n",
    "        skip = False\n",
    "        orders = hbt.orders(asset_no)\n",
    "        order_values = orders.values();\n",
    "        while order_values.has_next():\n",
    "            order = order_values.get()\n",
    "            if not order.cancellable:\n",
    "                skip = True\n",
    "                break\n",
    "        if skip:\n",
    "            continue\n",
    "        \n",
    "        depth = hbt.depth(asset_no)\n",
    "        position = hbt.position(asset_no)\n",
    "        \n",
    "        mid_tick = (depth.best_bid_tick + depth.best_ask_tick) / 2.0\n",
    "        mid_px = mid_tick * tick_size\n",
    "\n",
    "        #--------------------------------------------------------\n",
    "        # Computes bid price and ask price.\n",
    "        \n",
    "        notional_position_value = position * mid_px\n",
    "        normalized_position = notional_position_value / max_notional_position\n",
    "\n",
    "        relative_bid_depth = relative_half_spread + skew * normalized_position\n",
    "        relative_ask_depth = relative_half_spread - skew * normalized_position\n",
    "\n",
    "        bid_price_tick = min(np.floor(mid_tick * (1.0 - relative_bid_depth)), depth.best_bid_tick)\n",
    "        ask_price_tick = max(np.ceil(mid_tick * (1.0 + relative_ask_depth)), depth.best_ask_tick)\n",
    "\n",
    "        order_qty = max(round(order_notional_value / mid_px / lot_size) * lot_size, lot_size)\n",
    "        \n",
    "        #--------------------------------------------------------\n",
    "        # Updates quotes.\n",
    "        \n",
    "        # Creates a new grid for buy orders.\n",
    "        new_bid_orders = Dict.empty(np.uint64, np.float64)\n",
    "        if normalized_position < 1:\n",
    "            for i in range(grid_num):\n",
    "                # order price in tick is used as order id.\n",
    "                new_bid_orders[uint64(bid_price_tick)] = bid_price_tick * tick_size\n",
    "                \n",
    "                bid_price_tick -= grid_interval_tick\n",
    "\n",
    "        # Creates a new grid for sell orders.\n",
    "        new_ask_orders = Dict.empty(np.uint64, np.float64)\n",
    "        if normalized_position > -1:\n",
    "            for i in range(grid_num):\n",
    "                # order price in tick is used as order id.\n",
    "                new_ask_orders[uint64(ask_price_tick)] = ask_price_tick * tick_size\n",
    "\n",
    "                ask_price_tick += grid_interval_tick\n",
    "                \n",
    "        order_values = orders.values();\n",
    "        while order_values.has_next():\n",
    "            order = order_values.get()\n",
    "            # Cancels if a working order is not in the new grid.\n",
    "            if order.cancellable:\n",
    "                if (\n",
    "                    (order.side == BUY and order.order_id not in new_bid_orders)\n",
    "                    or (order.side == SELL and order.order_id not in new_ask_orders)\n",
    "                ):\n",
    "                    hbt.cancel(asset_no, order.order_id, False)\n",
    "                    \n",
    "        for order_id, order_price in new_bid_orders.items():\n",
    "            # Posts a new buy order if there is no working order at the price on the new grid.\n",
    "            if order_id not in orders:\n",
    "                hbt.submit_buy_order(asset_no, order_id, order_price, order_qty, GTX, LIMIT, False)\n",
    "                \n",
    "        for order_id, order_price in new_ask_orders.items():\n",
    "            # Posts a new sell order if there is no working order at the price on the new grid.\n",
    "            if order_id not in orders:\n",
    "                hbt.submit_sell_order(asset_no, order_id, order_price, order_qty, GTX, LIMIT, False)\n",
    "        \n",
    "        # Records the current state for stat calculation.\n",
    "        stat.record(hbt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4fa35bbd-3db9-4417-9eff-6fd6ac21efb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 45s, sys: 3.73 s, total: 1min 49s\n",
      "Wall time: 1min 49s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "roi_lb = 50000\n",
    "roi_ub = 150000\n",
    "\n",
    "grid_num = 1\n",
    "grid_interval_tick = 1\n",
    "\n",
    "asset = (\n",
    "    BacktestAsset()\n",
    "        .data(['BTCUSDT_20250801.npz'])\n",
    "        .linear_asset(1.0) \n",
    "        .intp_order_latency(['feed_order_latency_20250801.npz'])\n",
    "        .power_prob_queue_model(3)\n",
    "        .no_partial_fill_exchange()\n",
    "        .trading_value_fee_model(-0.00005, 0.0007)\n",
    "        .tick_size(0.1)\n",
    "        .lot_size(0.001)\n",
    "        .roi_lb(roi_lb)    \n",
    "        .roi_ub(roi_ub)\n",
    ")\n",
    "\n",
    "hbt = ROIVectorMarketDepthBacktest([asset])\n",
    "\n",
    "recorder = Recorder(1, 60_000_000)\n",
    "\n",
    "basic_mm(\n",
    "    hbt,\n",
    "    recorder.recorder,\n",
    "    relative_half_spread,\n",
    "    skew,\n",
    "    running_interval,\n",
    "    order_notional_value,\n",
    "    max_notional_position,\n",
    "    grid_num,\n",
    "    grid_interval_tick\n",
    ")\n",
    "\n",
    "_ = hbt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "409de936-1167-47fb-b9bd-0cf8e86ec610",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (1, 11)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>start</th><th>end</th><th>SR</th><th>Sortino</th><th>Return</th><th>MaxDrawdown</th><th>DailyNumberOfTrades</th><th>DailyTurnover</th><th>ReturnOverMDD</th><th>ReturnOverTrade</th><th>MaxPositionValue</th></tr><tr><td>datetime[μs]</td><td>datetime[μs]</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2025-08-01 00:00:00</td><td>2025-08-01 23:59:59</td><td>7.033126</td><td>10.104532</td><td>0.00119</td><td>0.00299</td><td>348.004028</td><td>17.399667</td><td>0.397857</td><td>0.000068</td><td>349253.6362</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (1, 11)\n",
       "┌───────────┬───────────┬──────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
       "│ start     ┆ end       ┆ SR       ┆ Sortino   ┆ … ┆ DailyTurn ┆ ReturnOve ┆ ReturnOve ┆ MaxPositi │\n",
       "│ ---       ┆ ---       ┆ ---      ┆ ---       ┆   ┆ over      ┆ rMDD      ┆ rTrade    ┆ onValue   │\n",
       "│ datetime[ ┆ datetime[ ┆ f64      ┆ f64       ┆   ┆ ---       ┆ ---       ┆ ---       ┆ ---       │\n",
       "│ μs]       ┆ μs]       ┆          ┆           ┆   ┆ f64       ┆ f64       ┆ f64       ┆ f64       │\n",
       "╞═══════════╪═══════════╪══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
       "│ 2025-08-0 ┆ 2025-08-0 ┆ 7.033126 ┆ 10.104532 ┆ … ┆ 17.399667 ┆ 0.397857  ┆ 0.000068  ┆ 349253.63 │\n",
       "│ 1         ┆ 1         ┆          ┆           ┆   ┆           ┆           ┆           ┆ 62        │\n",
       "│ 00:00:00  ┆ 23:59:59  ┆          ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "└───────────┴───────────┴──────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = recorder.get(0)\n",
    "stats = (\n",
    "    LinearAssetRecord(data)\n",
    "        .resample('1s')\n",
    "        .stats(book_size=max_notional_position)\n",
    ")\n",
    "stats.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7478de20-a333-4a8a-810b-44b8205e4098",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 4 Axes>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d916a30-c563-441a-ab50-8ce301bdf028",
   "metadata": {},
   "source": [
    "Firstly, accelerated backtest: 416 ms; full backtest: 1 min 49 s — roughly 260× faster.\n",
    "\n",
    "You can see that there are differences in the detailed numbers, but overall, the results show similar characteristics in terms of position and equity. The differences can become larger depending on the strategy’s characteristics—especially, as mentioned earlier, for assets where fills in the queue is crucial, such as those with a large tick size. Therefore, it is still important to verify the results from accelerated backtesting against those from full backtesting."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b956a9e1-8a97-400b-840d-2b40f367f86d",
   "metadata": {},
   "source": [
    "## Precompute Signal - Order Book Imbalance\n",
    "\n",
    "Similarly, precomputing the signal speeds up backtesting and allows rapid iteration to test ideas and tune parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e82d131b-8c1d-4227-8ba7-a4c009847266",
   "metadata": {},
   "outputs": [],
   "source": [
    "@njit\n",
    "def depth_below(depth, start, end, roi_lb_tick):\n",
    "    for i in range(start, end - 1, -1):\n",
    "        if depth[i - roi_lb_tick] > 0:\n",
    "            return i\n",
    "    return INVALID_MIN\n",
    "\n",
    "\n",
    "@njit\n",
    "def depth_above(depth, start, end, roi_lb_tick):\n",
    "    for i in range(start, end + 1):\n",
    "        if depth[i - roi_lb_tick] > 0:\n",
    "            return i\n",
    "    return INVALID_MAX\n",
    "\n",
    "@njit\n",
    "def precompute_obi(\n",
    "    tick_size,\n",
    "    lot_size,\n",
    "    roi_lb,\n",
    "    roi_ub,\n",
    "    end_ts,\n",
    "    local_ts, \n",
    "    depth_local_ts,\n",
    "    depth,\n",
    "    depth_range\n",
    "):\n",
    "    roi_lb_tick = round(roi_lb / tick_size)\n",
    "    roi_ub_tick = round(roi_ub / tick_size)\n",
    "    bid_depth = np.zeros(roi_ub_tick - roi_lb_tick, np.float64)\n",
    "    ask_depth = np.zeros(roi_ub_tick - roi_lb_tick, np.float64)\n",
    "\n",
    "    best_bid_tick = INVALID_MIN\n",
    "    best_ask_tick = INVALID_MAX\n",
    "    low_bid_tick = INVALID_MAX\n",
    "    high_ask_tick = INVALID_MIN\n",
    "    \n",
    "    depth_local_clock = Clock(depth_local_ts, 0)\n",
    "    local_clock = Clock(local_ts, 0)\n",
    "\n",
    "    out_t = 0\n",
    "    out_mid_tick = np.empty(len(local_ts), np.float64)\n",
    "    out_bid_qty = np.empty((len(local_ts), len(depth_range)), np.float64)\n",
    "    out_bid_weighted = np.empty((len(local_ts), len(depth_range)), np.float64)\n",
    "    out_ask_qty = np.empty((len(local_ts), len(depth_range)), np.float64)\n",
    "    out_ask_weighted = np.empty((len(local_ts), len(depth_range)), np.float64)\n",
    "    \n",
    "    while local_clock.ts <= end_ts:\n",
    "        ev = select_event(np.asarray([\n",
    "            depth_local_clock.ts,\n",
    "            local_clock.ts\n",
    "        ]))\n",
    "\n",
    "        if ev == -1:\n",
    "            raise ValueError\n",
    "        elif ev == 0:\n",
    "            # Builds the market depth.\n",
    "            side = depth[depth_local_clock.rn].side\n",
    "            px_tick = round(depth[depth_local_clock.rn].px / tick_size)\n",
    "\n",
    "            # Skips processing if the depth update price falls outside the defined range of interest.\n",
    "            if px_tick > roi_ub_tick or px_tick < roi_lb_tick:\n",
    "                depth_local_clock.next()\n",
    "                continue\n",
    "            \n",
    "            qty = depth[depth_local_clock.rn].qty\n",
    "            qty_lot = round(qty / lot_size)\n",
    "            \n",
    "            if side == 1:\n",
    "                bid_depth[px_tick - roi_lb_tick] = qty\n",
    "                if px_tick < low_bid_tick:\n",
    "                    low_bid_tick = px_tick\n",
    "                if px_tick > best_bid_tick and qty_lot > 0:\n",
    "                    # Updates the best bid if the bid price is higher than the current best bid.\n",
    "                    best_bid_tick = px_tick\n",
    "                    if best_bid_tick >= best_ask_tick:\n",
    "                        # When the best bid is greater than or equal to the best ask,\n",
    "                        # updates the best ask to the lowest ask above the new best bid.\n",
    "                        best_ask_tick = depth_above(ask_depth, best_bid_tick + 1, high_ask_tick, roi_lb_tick)\n",
    "                elif px_tick == best_bid_tick and qty_lot == 0:\n",
    "                    # Finds the new best bid if the current best bid is deleted.\n",
    "                    best_bid_tick = depth_below(bid_depth, px_tick, low_bid_tick, roi_lb_tick)\n",
    "            else:\n",
    "                ask_depth[px_tick - roi_lb_tick] = qty\n",
    "                if px_tick > high_ask_tick:\n",
    "                    high_ask_tick = px_tick\n",
    "                if px_tick < best_ask_tick and qty_lot > 0:\n",
    "                    # Updates the best ask if the ask price is lower than the current best ask.\n",
    "                    best_ask_tick = px_tick\n",
    "                    if best_ask_tick <= best_bid_tick:\n",
    "                        # When the best ask is less than or equal to the best bid,\n",
    "                        # updates the best bid to the highest bid below the new best ask.\n",
    "                        best_bid_tick = depth_below(bid_depth, best_ask_tick - 1, low_bid_tick, roi_lb_tick)\n",
    "                elif px_tick == best_ask_tick and qty_lot == 0:\n",
    "                    # Finds the best ask if the current best ask is deleted.\n",
    "                    best_ask_tick = depth_above(ask_depth, px_tick, high_ask_tick, roi_lb_tick)\n",
    "\n",
    "            depth_local_clock.next()\n",
    "        elif ev == 1:\n",
    "            if best_bid_tick == INVALID_MIN or best_ask_tick == INVALID_MAX:\n",
    "                mid_tick = np.nan\n",
    "                out_bid_qty[out_t, :] = np.nan\n",
    "                out_bid_weighted[out_t, :] = np.nan\n",
    "                out_ask_qty[out_t, :] = np.nan\n",
    "                out_ask_weighted[out_t, :] = np.nan\n",
    "            else:\n",
    "                mid_tick = (best_bid_tick + best_ask_tick) / 2\n",
    "    \n",
    "                # Computes the order book imbalance for the depth range from the mid-price.\n",
    "                # Please see https://hftbacktest.readthedocs.io/en/latest/tutorials/Market%20Making%20with%20Alpha%20-%20Order%20Book%20Imbalance.html\n",
    "                #\n",
    "                # To compute order-book imbalance values, aggregate over depth levels d, where d\n",
    "                # denotes the tick distance from the mid price; d is symmetric from the mid tick.\n",
    "                #   mid_tick, Sum{Q_bid[d]},  Sum{Q_ask[d]},  Sum{d * Q_bid[d]},  Sum{d * Q_ask[d]}\n",
    "                #\n",
    "                # VAMP = (Sum{P_bid[d] * Q_ask[d]} + Sum{P_ask[d] * Q_bid[d]}) / (Sum{Q_bid[d]} + Sum{Q_ask[d]})\n",
    "                #      = tick_size * (mid_tick * Sum{Q_ask[d]} - Sum{d * Q_ask[d]} + mid_tick * Sum{Q_bid[d]} + Sum{d * Q_bid[d]})\n",
    "                #        / (Sum{Q_bid[d]} + Sum{Q_ask[d]})\n",
    "                #\n",
    "                # Bid_effective = Sum{P_bid[d] * Q_bid[d]} / Sum{Q_bid[d]}\n",
    "                #               = tick_size * (mid_tick * Sum{Q_bid[d]} - Sum{d * Q_bid[d]}) / Sum{Q_bid[d]}\n",
    "                # Ask_effective = Sum{P_ask[d] * Q_ask[d]} / Sum{Q_ask[d]}\n",
    "                #               = tick_size * (mid_tick * Sum{Q_ask[d]} + Sum{d * Q_ask[d]}) / Sum{Q_ask[d]}\n",
    "                i = 0\n",
    "                bid_qty = 0.0\n",
    "                bid_weighted = 0.0\n",
    "                for d in range(best_bid_tick, low_bid_tick - 1, -1):\n",
    "                    bid_qty += bid_depth[d - roi_lb_tick]\n",
    "                    bid_weighted += bid_depth[d - roi_lb_tick] * (mid_tick - d)\n",
    "    \n",
    "                    if d < mid_tick * (1 - depth_range[i]):\n",
    "                        out_bid_qty[out_t, i] = bid_qty\n",
    "                        out_bid_weighted[out_t, i] = bid_weighted\n",
    "                        i += 1\n",
    "                        if i == len(depth_range):\n",
    "                            break\n",
    "    \n",
    "                i = 0\n",
    "                ask_qty = 0.0\n",
    "                ask_weighted = 0.0\n",
    "                for d in range(best_ask_tick, high_ask_tick + 1):\n",
    "                    ask_qty += ask_depth[d - roi_lb_tick]\n",
    "                    ask_weighted += ask_depth[d - roi_lb_tick] * (d - mid_tick)\n",
    "                    \n",
    "                    if d > mid_tick * (1 + depth_range[i]):\n",
    "                        out_ask_qty[out_t, i] = ask_qty\n",
    "                        out_ask_weighted[out_t, i] = ask_weighted\n",
    "                        i += 1\n",
    "                        if i == len(depth_range):\n",
    "                            break\n",
    "\n",
    "            out_mid_tick[out_t] = mid_tick\n",
    "    \n",
    "            out_t += 1\n",
    "            \n",
    "            local_clock.next()\n",
    "    return out_mid_tick[:out_t], out_bid_qty[:out_t], out_bid_weighted[:out_t], out_ask_qty[:out_t], out_ask_weighted[:out_t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6a9791c2-fd21-482a-bb5f-07a9b6bb3da5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_incremental_book(file):\n",
    "    df = pl.read_csv(file)\n",
    "    exch_ts = df['timestamp'].to_numpy() * 1000\n",
    "    local_ts = df['local_timestamp'].to_numpy() * 1000\n",
    "    data = df.select(\n",
    "        pl.col('is_snapshot').cast(pl.Int32),\n",
    "        pl.when(pl.col('side') == 'bid').then(1).otherwise(-1).alias('side'),\n",
    "        pl.col('price').alias('px'),\n",
    "        pl.col('amount').alias('qty'),\n",
    "    ).to_numpy(structured=True)\n",
    "    return exch_ts, local_ts, data\n",
    "\n",
    "# Because the BTCUSDT market depth data is very large, converting it using Polars often crashes due to memory limits.\n",
    "# Parsing directly in Python is slower but uses less memory.\n",
    "import gzip\n",
    "\n",
    "def load_incremental_book(file, buffer_size=200_000_000):\n",
    "    depth_dtype = np.dtype([\n",
    "        ('is_snapshot', np.int32),\n",
    "        ('side', np.int32),\n",
    "        ('px', np.float64),\n",
    "        ('qty', np.float64)\n",
    "    ])\n",
    "    \n",
    "    exch_ts = np.empty(buffer_size, np.int64)\n",
    "    local_ts = np.empty(buffer_size, np.int64)\n",
    "    data = np.empty(buffer_size, depth_dtype)\n",
    "    with gzip.open(file) as f:\n",
    "        header = True\n",
    "        i = 0\n",
    "        while True:\n",
    "            line = f.readline()\n",
    "            if not line:\n",
    "                break\n",
    "            if header:\n",
    "                header = False\n",
    "                continue\n",
    "            columns = line.decode().split(',')\n",
    "            if i == buffer_size:\n",
    "                raise MemoryError('Not enough buffer size to load data')\n",
    "            exch_ts[i] = int(columns[2]) * 1000\n",
    "            local_ts[i] = int(columns[3]) * 1000\n",
    "            data[i][0] = 1 if columns[4] == 'true' else 0\n",
    "            data[i][1] = 1 if columns[5] == 'bid' else -1\n",
    "            data[i][2] = float(columns[6])\n",
    "            data[i][3] = float(columns[7])\n",
    "            i += 1\n",
    "    return exch_ts[:i], local_ts[:i], data[:i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1b755e51-a563-4431-9ad8-a1cc094274ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "depth = load_incremental_book('BTCUSDT_incremental_book_L2_20250801.csv.gz')\n",
    "\n",
    "local_ts = np.arange(start_ts, end_ts + running_interval, running_interval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "131d4e29-8459-4fb8-b101-e55abc4cdbca",
   "metadata": {},
   "outputs": [],
   "source": [
    "roi_lb = 50000\n",
    "roi_ub = 150000\n",
    "\n",
    "depth_range = np.asarray([0.0025, 0.005, 0.0075, 0.01, 0.015, 0.02, 0.025, 0.03])\n",
    "\n",
    "(\n",
    "    out_mid_tick,\n",
    "    out_bid_qty,\n",
    "    out_bid_weighted,\n",
    "    out_ask_qty,\n",
    "    out_ask_weighted\n",
    ") = precompute_obi(\n",
    "    tick_size,\n",
    "    lot_size,\n",
    "    roi_lb,\n",
    "    roi_ub,\n",
    "    end_ts,\n",
    "    local_ts, \n",
    "    depth[1],\n",
    "    depth[2],\n",
    "    depth_range\n",
    ")\n",
    "\n",
    "table_def = {\n",
    "    'local_ts': local_ts,\n",
    "    'mid_tick': out_mid_tick\n",
    "}\n",
    "for i, d in enumerate(depth_range):\n",
    "    table_def[f'bid_qty_{d}'] = out_bid_qty[:, i]\n",
    "    table_def[f'bid_weighted_{d}'] = out_bid_weighted[:, i]\n",
    "    table_def[f'ask_qty_{d}'] = out_ask_qty[:, i]\n",
    "    table_def[f'ask_weighted_{d}'] = out_ask_weighted[:, i]\n",
    "\n",
    "pq.write_table(pa.table(table_def), f'BTCUSDT_obi_20250801.parquet', compression='zstd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "02f96fd4-0625-4faf-8f26-b9a54c020a8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_17848/2337632004.py:20: RuntimeWarning: invalid value encountered in divide\n",
      "  vamp = np.divide(\n",
      "/tmp/ipykernel_17848/2337632004.py:28: RuntimeWarning: invalid value encountered in divide\n",
      "  bid_eff = tick_size * (mid_tick * bid_qty - bid_weighted) / bid_qty\n",
      "/tmp/ipykernel_17848/2337632004.py:29: RuntimeWarning: invalid value encountered in divide\n",
      "  ask_eff = tick_size * (mid_tick * ask_qty + ask_weighted) / ask_qty\n"
     ]
    }
   ],
   "source": [
    "table = pq.read_table('BTCUSDT_obi_20250801.parquet')\n",
    "local_ts = table['local_ts'].to_numpy()\n",
    "mid_tick = table['mid_tick'].to_numpy()\n",
    "\n",
    "# VAMP = (Sum{P_bid[d] * Q_ask[d]} + Sum{P_ask[d] * Q_bid[d]}) / (Sum{Q_bid[d]} + Sum(Q_ask[d])}\n",
    "# Sum{P_bid[d] * Q_ask[d]} + Sum{P_ask[d] * Q_bid[d]} \n",
    "# = tick_size * (mid_tick * Sum{Q_ask[d]} - Sum{d * Q_ask[d]} + mid_tick * Sum{Q_bid[d]} + Sum{d * Q_bid[d]})\n",
    "#\n",
    "# P_effective_bid = Sum{P_bid[d] * Q_bid[d]} / Sum{Q_bid[d]}\n",
    "# Sum{P_bid[d] * Q_bid[d]}\n",
    "# = tick_size * (mid_tick * Sum{Q_bid[d]} - Sum{d * Q_bid[d]})\n",
    "\n",
    "depth_range = 0.005\n",
    "\n",
    "bid_qty = table[f'bid_qty_{depth_range}'].to_numpy()\n",
    "ask_qty = table[f'ask_qty_{depth_range}'].to_numpy()\n",
    "bid_weighted = table[f'bid_weighted_{depth_range}'].to_numpy()\n",
    "ask_weighted = table[f'ask_weighted_{depth_range}'].to_numpy()\n",
    "\n",
    "vamp = np.divide(\n",
    "    tick_size * (\n",
    "        mid_tick * ask_qty - ask_weighted\n",
    "        + mid_tick * bid_qty + bid_weighted\n",
    "    ),\n",
    "    bid_qty + ask_qty\n",
    ")\n",
    "\n",
    "bid_eff = tick_size * (mid_tick * bid_qty - bid_weighted) / bid_qty\n",
    "ask_eff = tick_size * (mid_tick * ask_qty + ask_weighted) / ask_qty\n",
    "vamp_eff = np.divide(\n",
    "    bid_eff * ask_qty + ask_eff * bid_qty,\n",
    "    bid_qty + ask_qty\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7f24aa45-3a6a-47bc-a6c0-7150d33d96fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd563f66450>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "start = 35000\n",
    "end = 40000\n",
    "\n",
    "ts = pl.Series(\"timestamp\", local_ts)\n",
    "ts_dt = ts.cast(pl.Datetime('ns'))\n",
    "\n",
    "plt.plot(ts_dt[start:end], vamp[start:end])\n",
    "plt.plot(ts_dt[start:end], vamp_eff[start:end])\n",
    "plt.plot(ts_dt[start:end], mid_tick[start:end] * tick_size)\n",
    "plt.legend(['VAMP', 'VAMP$_{effective}$', 'Mid Price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "478b4756-2765-4c5f-adf9-19b0194c8026",
   "metadata": {},
   "source": [
    "## Accelerated Backtesting - Order Book Imbalance\n",
    "\n",
    "Using the precomputed order book imbalance, we perform accelerated backtesting. The only modification is that quoting based on `mid_tick` is replaced with `fair_px_tick`. \n",
    "\n",
    "$VAMP_{effective}$ is selected for demonstration purposes. As illustrated in [Market Making with Alpha - Order Book Imbalance](https://hftbacktest.readthedocs.io/en/latest/tutorials/Market%20Making%20with%20Alpha%20-%20Order%20Book%20Imbalance.html), there are many different ways to compute order book imbalance. The key is to identify which formulation provides the stronger signal and which parameters lead to better performance, as these may vary depending on the strategy and how the signal is monetized.\n",
    "\n",
    "At the end of this section, you can observe that performance improves compared to `mid_tick`; however, it is ultimately necessary to evaluate results over longer periods. Such comparisons and analyses require repeated, iterative backtesting — made feasible through this accelerated backtesting framework."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2c27de3b-806b-479b-982d-104f81f685bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "@njit\n",
    "def accelerated_backtest(\n",
    "    fair_px_tick,\n",
    "    relative_half_spread,\n",
    "    skew,\n",
    "    order_notional_value,\n",
    "    max_notional_position,\n",
    "    fee,\n",
    "    tick_size,\n",
    "    lot_size,\n",
    "    local_ts,\n",
    "    best_bid_tick,\n",
    "    best_ask_tick,\n",
    "    bid_fill_tick,\n",
    "    ask_fill_tick,\n",
    "    order_ack_ts,\n",
    "    bid_fill_tick_ack,\n",
    "    ask_fill_tick_ack,\n",
    "    best_bid_tick_ack,\n",
    "    best_ask_tick_ack,\n",
    "    bid_fill_tick_after_ack,\n",
    "    ask_fill_tick_after_ack\n",
    "):\n",
    "    # req_bid_tick: bid order price in ticks (limit buy order with GTX) sent to the exchange, before the exchange acknowledges it.\n",
    "    # req_ask_tick: ask order price in ticks (limit sell order with GTX) sent to the exchange, before the exchange acknowledges it.\n",
    "    # open_bid_tick: bid order price in ticks acknowledged by the exchange, currently an open order in the market.\n",
    "    # open_ask_tick: ask order price in ticks acknowledged by the exchange, currently an open order in the market.\n",
    "    #\n",
    "    # INVALID_MIN and INVALID_MAX indicate that there are no orders.\n",
    "    #\n",
    "    # Example:\n",
    "    #   If req_bid_tick is INVALID_MIN and there is an open bid order, \n",
    "    #   the open bid order will be canceled (if the cancel request reaches the exchange before the order is filled).\n",
    "    #   When an order is filled, its price is set to INVALID_MIN or INVALID_MAX accordingly.\n",
    "    req_bid_tick = open_bid_tick = INVALID_MIN\n",
    "    req_ask_tick = open_ask_tick = INVALID_MAX\n",
    "    # corresponding order quantities.\n",
    "    open_bid_qty = req_bid_qty = 0.0\n",
    "    open_ask_qty = req_ask_qty = 0.0\n",
    "\n",
    "    # Initial state.\n",
    "    balance = 0.0\n",
    "    position = 0.0\n",
    "    num_trades = 0\n",
    "    trading_value = 0.0\n",
    "    trading_volume = 0.0\n",
    "\n",
    "    # Row index iterator\n",
    "    t = 0\n",
    "\n",
    "    # State record for stats\n",
    "    rec_i = 0\n",
    "    record = np.empty(len(local_ts), record_dtype)\n",
    "    \n",
    "    while True:\n",
    "        #--------------------------------------------------------\n",
    "        # Local bot logic at `local_ts[t]`.\n",
    "        mid_tick = (best_bid_tick[t] + best_ask_tick[t]) / 2.0\n",
    "        mid_px = mid_tick * tick_size\n",
    "        \n",
    "        notional_position_value = position * mid_px\n",
    "        normalized_position = notional_position_value / max_notional_position\n",
    "\n",
    "        relative_bid_depth = relative_half_spread + skew * normalized_position\n",
    "        relative_ask_depth = relative_half_spread - skew * normalized_position\n",
    "        \n",
    "        req_bid_tick = min(np.floor(fair_px_tick[t] * (1.0 - relative_bid_depth)), best_bid_tick[t])\n",
    "        req_ask_tick = max(np.ceil(fair_px_tick[t] * (1.0 + relative_ask_depth)), best_ask_tick[t])\n",
    "\n",
    "        req_bid_qty = req_ask_qty = max(round(order_notional_value / mid_px / lot_size) * lot_size, lot_size)\n",
    "        \n",
    "        # If the position exceeds the risk limit (max notional position),\n",
    "        # no orders shall be open in that direction.\n",
    "        if normalized_position > 1:\n",
    "            req_bid_tick = INVALID_MIN\n",
    "        if normalized_position < -1:\n",
    "            req_ask_tick = INVALID_MAX\n",
    "\n",
    "        #--------------------------------------------------------\n",
    "        # Records the current state.\n",
    "        record[rec_i].timestamp = local_ts[t]\n",
    "        record[rec_i].price = mid_tick * tick_size\n",
    "        record[rec_i].position = position\n",
    "        record[rec_i].balance = balance * tick_size\n",
    "        record[rec_i].fee = trading_value * tick_size * fee\n",
    "        record[rec_i].num_trades = num_trades\n",
    "        record[rec_i].trading_volume = trading_volume\n",
    "        record[rec_i].trading_value = trading_value * tick_size\n",
    "        \n",
    "        rec_i += 1\n",
    "\n",
    "        #--------------------------------------------------------\n",
    "        # Processes the exchange-side logic (order fill logic).\n",
    "        \n",
    "        # If any of the requested order prices differ from the open order's price,\n",
    "        # it is assumed that the bot sent the order request.\n",
    "        # The request will be acknowledged and processed at `order_ack_ts[t]`.\n",
    "        # Otherwise, check if the open order is filled.\n",
    "        if req_bid_tick != open_bid_tick or req_ask_tick != open_ask_tick:\n",
    "            # The current time is `order_ack_ts[t]`.\n",
    "            order_ack_ts_ = order_ack_ts[t]\n",
    "\n",
    "            # If there are open orders with valid prices,\n",
    "            # checks whether they are filled before accepting the newly requested orders.\n",
    "            if open_bid_tick > INVALID_MIN and open_bid_tick >= bid_fill_tick_ack[t]:\n",
    "                execute_value = open_bid_tick * open_bid_qty\n",
    "                balance -= execute_value\n",
    "                position += open_bid_qty\n",
    "                num_trades += 1\n",
    "                trading_volume += open_bid_qty\n",
    "                trading_value += execute_value\n",
    "                # Invalidates the price because the order is filled.\n",
    "                open_bid_tick = INVALID_MIN\n",
    "            if open_ask_tick < INVALID_MAX and open_ask_tick <= ask_fill_tick_ack[t]:\n",
    "                execute_value = open_ask_tick * open_ask_qty\n",
    "                balance += execute_value\n",
    "                position -= open_ask_qty\n",
    "                num_trades += 1\n",
    "                trading_volume += open_ask_qty\n",
    "                trading_value += execute_value\n",
    "                # Invalidates the price because the order is filled.\n",
    "                open_ask_tick = INVALID_MAX\n",
    "            \n",
    "            # New orders are treated as GTX. \n",
    "            # If the requested buy order price is greater than or equal to the best ask,\n",
    "            # or the requested sell order price is less than or equal to the best bid, \n",
    "            # the orders are rejected.\n",
    "            # Invalidates the price if the order is rejected.\n",
    "            if req_bid_tick >= best_ask_tick_ack[t]:\n",
    "                req_bid_tick = INVALID_MIN\n",
    "            if req_ask_tick <= best_bid_tick_ack[t]:\n",
    "                req_ask_tick = INVALID_MAX\n",
    "\n",
    "            # Updates the open orders to reflect accepted orders.\n",
    "            open_bid_tick = req_bid_tick\n",
    "            open_ask_tick = req_ask_tick\n",
    "            open_bid_qty = req_bid_qty\n",
    "            open_ask_qty = req_ask_qty\n",
    "\n",
    "            # If there are open orders with valid prices,\n",
    "            # checks whether they are filled before the next local timestamp (`local_ts[t+n]`)\n",
    "            # that is greater than the current timestamp (`order_ack_ts[t]`).\n",
    "            if open_bid_tick > INVALID_MIN and open_bid_tick >= bid_fill_tick_after_ack[t]:\n",
    "                execute_value = open_bid_tick * open_bid_qty\n",
    "                balance -= execute_value\n",
    "                position += open_bid_qty\n",
    "                num_trades += 1\n",
    "                trading_volume += open_bid_qty\n",
    "                trading_value += execute_value\n",
    "                # Invalidates the price because the order is filled.\n",
    "                open_bid_tick = INVALID_MIN\n",
    "            if open_ask_tick < INVALID_MAX and open_ask_tick <= ask_fill_tick_after_ack[t]:\n",
    "                execute_value = open_ask_tick * open_ask_qty\n",
    "                balance += execute_value\n",
    "                position -= open_ask_qty\n",
    "                num_trades += 1\n",
    "                trading_volume += open_ask_qty\n",
    "                trading_value += execute_value\n",
    "                # Invalidates the price because the order is filled.\n",
    "                open_ask_tick = INVALID_MAX\n",
    "\n",
    "            # The next local timestamp must be greater than the current timestamp (`order_ack_ts[t]`).\n",
    "            while t < len(local_ts) and local_ts[t] < order_ack_ts_:\n",
    "                t += 1\n",
    "            # Breaks if no more rows remain for processing.\n",
    "            if t == len(local_ts):\n",
    "                break\n",
    "        else:\n",
    "            # Checks if the open orders are filled between two local timestamps.\n",
    "            # The next row of data contains the bid fill price (in ticks) and ask fill price (in ticks) \n",
    "            # for that interval (step).\n",
    "            t += 1\n",
    "            # Breaks if no more rows remain for processing.\n",
    "            if t == len(local_ts):\n",
    "                break\n",
    "\n",
    "            # # If there are open orders with valid prices, checks if they are filled.\n",
    "            if open_bid_tick > INVALID_MIN and open_bid_tick >= bid_fill_tick[t]:\n",
    "                execute_value = open_bid_tick * open_bid_qty\n",
    "                balance -= execute_value\n",
    "                position += open_bid_qty\n",
    "                num_trades += 1\n",
    "                trading_volume += open_bid_qty\n",
    "                trading_value += execute_value\n",
    "                # Invalidates the price because the order is filled.\n",
    "                open_bid_tick = INVALID_MIN\n",
    "            if open_ask_tick < INVALID_MAX and open_ask_tick <= ask_fill_tick[t]:\n",
    "                execute_value = open_ask_tick * open_ask_qty\n",
    "                balance += execute_value\n",
    "                position -= open_ask_qty\n",
    "                num_trades += 1\n",
    "                trading_volume += open_ask_qty\n",
    "                trading_value += execute_value\n",
    "                # Invalidates the price because the order is filled.\n",
    "                open_ask_tick = INVALID_MAX\n",
    "        \n",
    "    return record[:rec_i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "de540528-4d3d-4484-9e22-369fa37802d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 241 ms, sys: 10.9 ms, total: 252 ms\n",
      "Wall time: 250 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "record = accelerated_backtest(\n",
    "        vamp_eff / tick_size,\n",
    "        relative_half_spread,\n",
    "        skew,\n",
    "        order_notional_value,\n",
    "        max_notional_position,\n",
    "        fee_per_value,\n",
    "        tick_size,\n",
    "        lot_size,\n",
    "        local_ts,\n",
    "        best_bid_tick,\n",
    "        best_ask_tick,\n",
    "        bid_fill_tick,\n",
    "        ask_fill_tick,\n",
    "        order_ack_ts,\n",
    "        bid_fill_tick_ack,\n",
    "        ask_fill_tick_ack,\n",
    "        best_bid_tick_ack,\n",
    "        best_ask_tick_ack,\n",
    "        bid_fill_tick_after_ack,\n",
    "        ask_fill_tick_after_ack\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e364d324-6fb2-4aff-9a42-6af34b2efb25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (1, 11)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>start</th><th>end</th><th>SR</th><th>Sortino</th><th>Return</th><th>MaxDrawdown</th><th>DailyNumberOfTrades</th><th>DailyTurnover</th><th>ReturnOverMDD</th><th>ReturnOverTrade</th><th>MaxPositionValue</th></tr><tr><td>datetime[μs]</td><td>datetime[μs]</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2025-08-01 00:00:00</td><td>2025-08-02 00:00:00</td><td>18.879569</td><td>26.861534</td><td>0.018396</td><td>0.012351</td><td>1492.0</td><td>74.602088</td><td>1.489513</td><td>0.000247</td><td>1.0547e6</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (1, 11)\n",
       "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
       "│ start     ┆ end       ┆ SR        ┆ Sortino   ┆ … ┆ DailyTurn ┆ ReturnOve ┆ ReturnOve ┆ MaxPosit │\n",
       "│ ---       ┆ ---       ┆ ---       ┆ ---       ┆   ┆ over      ┆ rMDD      ┆ rTrade    ┆ ionValue │\n",
       "│ datetime[ ┆ datetime[ ┆ f64       ┆ f64       ┆   ┆ ---       ┆ ---       ┆ ---       ┆ ---      │\n",
       "│ μs]       ┆ μs]       ┆           ┆           ┆   ┆ f64       ┆ f64       ┆ f64       ┆ f64      │\n",
       "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
       "│ 2025-08-0 ┆ 2025-08-0 ┆ 18.879569 ┆ 26.861534 ┆ … ┆ 74.602088 ┆ 1.489513  ┆ 0.000247  ┆ 1.0547e6 │\n",
       "│ 1         ┆ 2         ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
       "│ 00:00:00  ┆ 00:00:00  ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
       "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from hftbacktest.stats import LinearAssetRecord\n",
    "\n",
    "stats = (\n",
    "    LinearAssetRecord(record)\n",
    "        .resample('1s')\n",
    "        .stats(book_size=max_notional_position)\n",
    ")\n",
    "stats.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ae3b7d09-f503-4a2f-9673-b1dae04c453d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 4 Axes>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a60b0f27-239d-445e-afd5-09a47dd9af58",
   "metadata": {},
   "source": [
    "In the next tutorial, we will explore a more generalized framework to forecasting and fair value pricing in research based on [A Comprehensive Framework for Pricing Models](https://hftbacktest.readthedocs.io/en/latest/tutorials/Market%20Making%20with%20Alpha%20-%20APT.html#A-Comprehensive-Framework-for-Pricing-Models)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
